In addition, the output phone sequence from ASR, e.g. end-to-end models [1]}, [2]}, [3]} or extended recognition network [4]}, [5]}, can be aligned with the given prompts, and a phone or word is marked as mispronounced if there is some mismatch between these two sequences. Alternatively, the two-step approaches treat pronunciation scoring or mispronunciation detection as regression or classification task. Specifically, phone, word and sentence boundaries are first generated by forced-alignment, and then either frame-level or segmental-level pronunciation features within each boundary are fed into task-dependent classifiers or regressors (e.g., [6]}, [7]}, [8]}, [9]}, [10]}, [11]}, [12]}, [13]}, [14]}, [15]}, [16]}). Finally, the posterior probabilities or predicted values obtained from those models are often used as pronunciation scores. Taking advantage of using extra supervised training data, the two-step approach armed with deep learning techniques was reported to achieve better scoring performance than its counterparts [10]}.
